We all know that life is uncertain, and
that uncertainty especially applies to personal finances. That’s why
the financial planning community is increasingly employing a technique
that illustrates to clients the “odds” that a given financial
strategy will prove successful.

Take investing for retirement. When
calculating how much you might need to save monthly in order to
achieve a certain lifestyle upon retirement, you might assume a
certain dollar size for your nest egg, an average annual return on
your investments, and the number of years you have to save. For
example, you might assume a $500,000 nest egg and an eight percent
return for 20 years. From that, it’s simple to calculate how much
you should save each month to build your nest egg.

The problem is, the odds are it won’t
happen. It’s an illusion of stability. That’s because nothing
happens every year exactly as you calculate. Investment returns, for
example, might average 8 percent over those 20 years. However, during
that time returns will vary one year to the next: perhaps 20 percent
one year, 6 percent another, a 7 percent loss another. Similarly, the
temperature for a given day probably won’t be exactly the average
temperature for that day. Unlike the daily weather, however, the
sequence in which investment returns occur can make a huge difference
in how large your nest egg really turns out to be. If returns are
higher than average early on in the accumulation phase, the nest egg
will probably be larger than you projected; lower-than-average returns
early on means it will likely be smaller than you planned.

The same approach applies to withdrawal
rates from your nest egg. How much you can safely withdraw each year
and not run out of money before your death will depend on, among other
factors, investment returns, the inflation rate, and whether you live
longer or shorter than your life expectancy.

To get a better feel of what your
chances really are for achieving a particular financial goal, planners
are turning to a computer modeling technique called Monte Carlo
simulation. Say you want to know how much you can realistically
withdraw from your retirement portfolio each year and not run out of
money (three percent? Five? Eight?). The planner plugs in the desired
withdrawal rate, the “expected” return of the investments, the
portfolio’s standard deviation (how much the returns might vary each
year), an expected inflation rate, life expectancy and so on. The
program then generates hundreds, even thousands of variations of these
numbers, each generation slightly altering a particular variable such
as the sequence of investment returns, the average rate of return or a
different life expectancy, while keeping the target withdrawal rate
the same.

The result shows you the probability,
given the assumptions used, that a particular withdrawal rate will
achieve the results you want. For example, you might find that you
have a 90 percent chance of not running out of money by withdrawing no
more than 4 percent from your portfolio each year in retirement—but
only a 75 percent chance if you consistently withdraw 5 percent. This
would be similar to the weather reporter saying the likelihood of rain
today is 75 percent, which might encourage you to take an umbrella.
The decision whether to take a particular level of financial risk, of
course, is ultimately yours. Keep in mind that Monte Carlo
calculations are very useful in assessing risk, the calculations are
based upon historical relationships which may or may not hold true
in the future.

While Monte Carlo simulation is being
applied most prominently to retirement planning, it can be used for
any type of financial decision that involves uncertainty. For example,
you might use it to determine the odds that a particular funding rate
for a vanishing premium life insurance policy is sufficient or how
much you should invest each year to build a college fund. Monte Carlo
is not foolproof. The assumptions one plugs into the program need to
be realistic, and there are inevitably variables left out of the
calculations. Variables are usually generated from long-run historical
norms. But what if the future is vastly different? What happens if you
experience a major financial crisis, such as an illness or loss of a
job? This could change your financial picture dramatically.

Ultimately, Monte Carlo is a useful
tool that provides a more accurate picture of a financial strategy.
But it doesn’t guarantee results. Nothing substitutes for common
sense, years of experience in working with retirees, and a realistic overall financial plan that prepares for the
uncertainties along the way.